Implicit Sensor-based Authentication of Smartphone Users with Smartwatch

Smartphones are now frequently used by end-users as the portals to cloud-based services, and smartphones are easily stolen or co-opted by an attacker. Beyond the initial log-in mechanism, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data, whether in the cloud or in the smartphone. But attackers who have gained access to a logged-in smartphone have no incentive to re-authenticate, so this must be done in an automatic, non-bypassable way. Hence, this paper proposes a novel authentication system, iAuth, for implicit, continuous authentication of the end-user based on his or her behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We design a system that gives accurate authentication using machine learning and sensor data from multiple mobile devices. Our system can achieve 92.1% authentication accuracy with negligible system overhead and less than 2% battery consumption.Comment: Published in Hardware and Architectural Support for Security and Privacy (HASP), 201

Banach spaces with polynomial numerical index 1

We characterize Banach spaces with polynomial numerical index 1 when they have the Radon-Nikod\'ym property. The holomorphic numerical index is introduced and the characterization of the Banach space with holomorphic numerical index 1 is obtained when it has the Radon-Nikod\'ym property

The politics of evidence: ‘Doing nothing’ about LGBT health inequities by the WHO

How is ‘nothing’ produced and justified, and how is it functioning? Here, I will take a multilateral debate in the World Health Organisation (WHO) over the issues regarding health inequities experienced by sexual and gender minorities as an example. This article asks what national delegates really mean when they blame on the lack of evidence? Observing the debates in the WHO and elsewhere, what certain national governments have been doing is to avoid – by not making anything happen – a potential formulation of future international pressure through global health policymaking and its normative discourse. Through deconstructing the discourse of a ‘lack of evidence’, I identify the socio-political functions of ignorance and ignoring. That is, they did nothing, not because they didn’t understand and care. Quite on the contrary, it was because they cared and knew too well that health is always political, and yet, it is not just the politics concerning knowledge production and media representation; it is also international politics

Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier network to become more robust to adversarial perturbations. Furthermore, our adversarial training framework efficiently reduces overfitting and outperforms other regularization methods such as Dropout. We applied our method to supervised learning for CIFAR datasets, and experimantal results show that our method significantly lowers the generalization error of the network. To the best of our knowledge, this is the first method which uses GAN to improve supervised learning

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352